RepoSage

RepoSage is a codebase intelligence tool that leverages Retrieval-Augmented Generation (RAG) to help developers understand and navigate repositories. By ingesting a GitHub repository and building a local vector index, RepoSage allows users to ask natural language questions about codebase architecture, data flows, dependencies, and logic.

Features

• Direct Repository Ingestion: Clone and index public or private GitHub repositories directly from a URL.
• Code-Aware Parsing: Processes and chunks source code files specifically structured for optimal context retrieval.
• Local Vector Search: Utilizes HuggingFace embeddings and ChromaDB for fast, local vector storage and similarity search.
• Intelligent Chat Interface: Powered by LangChain and Groq (Llama 3.1) to synthesize accurate technical answers based on your code.
• Source Attribution: Responses include direct references to the specific files used to generate the answer.

Tech Stack

• Frontend: Streamlit
• LLM Orchestration: LangChain
• LLM Provider: Groq (Llama 3.1)
• Vector Database: ChromaDB
• Embeddings: HuggingFace

Getting Started

Prerequisites

• Python 3.9 or higher
• A Groq API Key
• (Optional) A GitHub Personal Access Token for accessing private repositories

Installation

1. Clone this repository to your local machine:

   git clone [https://github.com/yourusername/RepoSage.git](https://github.com/yourusername/RepoSage.git)
   cd RepoSage

2. Install the required Python dependencies:

   pip install -r requirements.txt

3. Configure your environment variables. Create a .env file in the root directory and add your API keys:

   GROQ_API_KEY=your_groq_api_key_here

Usage

1. Launch the application using Streamlit:

   streamlit run app.py

2. Navigate to the provided local URL (typically http://localhost:8501) in your web browser.

3. In the sidebar, enter the target GitHub repository URL (and your access token if the repository is private).

4. Click Load Repository and wait for the indexing process to complete.

5. Once indexed, use the main chat interface to ask technical questions about the loaded codebase.

Project Structure

• app.py: The main Streamlit application handling the UI and state management.
• rag_engine.py: Contains the logic for building the ChromaDB vector store and the LangChain RAG pipeline.
• repo_ingestion.py: Handles cloning the target GitHub repository, parsing the directories, and chunking the files for embedding.